A mathematical model for fitness effects on viral persistence

Persistent viral infections arise from complex interactions between viral replication, host cell responses, and ongoing viral evolution. A general framework linking viral fitness to persistence dynamics is lacking. Here, we develop for the first time a mathematical model of viral persistence that takes into consideration viral fitness variations. Essential to the model is the partition of the classical fitness parameter into three components: replicative, infective, and dispersal fitness. The model was initially inspired by a new experiment on hepatitis C virus (HCV) persistence, established in human hepatoma cells, also reported in this work. This experiment documents two strikingly different viral trajectories depending on the initial replicative fitness of the viral population used to establish persistence. The dynamical model describes the interactions among uninfected cells, infected cells, and infectious virions, and it incorporates, through a continuum, two alternative mechanisms of viral release from cells: budding and lysis. Analysis of the model reveals that viral fitness parameters organise infection outcomes into distinct dynamical regimes. Low replicative and dispersal fitness values lead to viral extinction, whereas high values enable persistence through either stable coexistence or recurrent infection waves. The space of fitness discloses a hierarchy among these parameters, with replicative and dispersal fitness being able to trigger important shifts in the outcome of the infection as opposed to infective fitness. The model identifies trade-offs between replication and dispersal that shape viral production and predicts slow dynamical regimes in which infection may persist despite low detectable viral loads. These dynamical transitions provide candidate mechanisms capable of generating the persistence patterns observed experimentally. Our results establish a computational framework linking multidimensional viral fitness to persistence dynamics and suggest general principles by which evolving RNA viruses transition between extinction (cell curing) and sustained persistence. Author summary Persistent viral infections depend not only on how efficiently a virus replicates, but also on its ability to infect new cells and spread through the surrounding environment. However, mathematical models usually represent these components of viral fitness with a single parameter, limiting our understanding of how viral evolution influences infection persistence. Here, we combine experiments with a mathematical model that separates viral fitness into three components: replication, infection, and dispersal. The model was inspired by experiments with hepatitis C virus populations maintained in human hepatoma cell cultures, which displayed markedly different persistence patterns depending on the replicative fitness of the initial virus. Our results show that replication and dispersal play the dominant roles in determining whether a viral population becomes extinct or persists through stable coexistence or recurrent waves of infection. The model also reveals trade-offs between these fitness components and predicts slowly evolving infections that can persist despite maintaining very low viral loads. By capturing experimentally observed fluctuations, the model identifies dynamical mechanisms that can qualitatively generate the abrupt and progressive losses of persistence observed in hepatitis C virus populations. This framework may help identify general principles governing transitions between extinction and long-term persistence in evolving RNA viruses.